Exploring Atrial Fibrillation: Understanding the Complex Relation Between Lifestyle and Genetic Factors
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are the leading cause of death worldwide across diverse ethnic groups. Among these, atrial fibrillation (AF) stands as one of the most prevalent types of arrhythmias and the primary cause of stroke. Risk factors associated with AF include alcohol consumption, aging, high blood pressure, hypertension, inflammation, and genetic factors. A family history of CVD could indicate an increased risk. Consequently, genetic, and genomic testing should be performed to identify the molecular etiology of CVDs and assess at-risk patients. It is important to note that CVDs are the results of the complex interplay of genes and environmental factors, including ethnicity. In this case, the proband's clinic story includes a history of smoking abuse for 10 years (10 cigarettes per day), obesity, hypertension, and an associated familial history. These risk factors, along with genetic variants, could trigger the early onset of AF. In recent years, genetic and genomic studies have significantly advanced our understanding of CVD etiology, given that next-generation sequencing (NGS) allows for the identification of genetic variants that could contribute to these pathologies. Furthermore, NGS facilitates early diagnosis, personalized pharmacological approaches, and identification of novel biomarkers. Thus, NGS is a valuable tool in CVD management. However, such studies are limited in Ecuador, a low- and middle-income country. Several challenges contribute to this gap, encompassing economic, infrastructural, and educational obstacles. Notably, the cost of genetic and genomic studies may also pose a barrier, restricting access to a portion of the population. In this case report, we present a 56-year-old Ecuadorian woman, who has been diagnosed with AF; however, after performing NGS no disease-associated variants were found, despite having strong clinical signs and symptoms. In summary, this case report contributes valuable insights into the complex interplay between genetic and lifestyle factors in the development and management of AF. The case report aims to underscore the potential impact of genetic variants on disease risk, even when classified as variants of uncertain significance, and the importance of an integral approach to patient care that includes genetic screening, lifestyle interventions, and tailored pharmacological treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".